{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Issue#133 Incorrect Display of Single Candle (or OHLC Bar)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>132.089996</td>\n",
       "      <td>134.100006</td>\n",
       "      <td>131.779999</td>\n",
       "      <td>133.919998</td>\n",
       "      <td>117.161659</td>\n",
       "      <td>202385700</td>\n",
       "      <td>132.373927</td>\n",
       "      <td>125.316073</td>\n",
       "      <td>1.219057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-05</th>\n",
       "      <td>133.779999</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>133.809998</td>\n",
       "      <td>117.065437</td>\n",
       "      <td>165936000</td>\n",
       "      <td>133.254297</td>\n",
       "      <td>124.912703</td>\n",
       "      <td>1.066618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2011-07-01  132.089996  134.100006  131.779999  133.919998  117.161659   \n",
       "2011-07-05  133.779999  134.080002  133.389999  133.809998  117.065437   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2011-07-01  202385700  132.373927  125.316073  1.219057  \n",
       "2011-07-05  165936000  133.254297  124.912703  1.066618  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>132.289993</td>\n",
       "      <td>132.990005</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>132.789993</td>\n",
       "      <td>118.641281</td>\n",
       "      <td>169242100</td>\n",
       "      <td>136.500761</td>\n",
       "      <td>128.219241</td>\n",
       "      <td>0.551922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>135.199997</td>\n",
       "      <td>136.270004</td>\n",
       "      <td>134.850006</td>\n",
       "      <td>136.100006</td>\n",
       "      <td>121.598610</td>\n",
       "      <td>212250900</td>\n",
       "      <td>136.721010</td>\n",
       "      <td>128.792993</td>\n",
       "      <td>0.921670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2012-06-28  132.289993  132.990005  131.279999  132.789993  118.641281   \n",
       "2012-06-29  135.199997  136.270004  134.850006  136.100006  121.598610   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2012-06-28  169242100  136.500761  128.219241  0.551922  \n",
       "2012-06-29  212250900  136.721010  128.792993  0.921670  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
    "daily.index.name = 'Date'\n",
    "daily.shape\n",
    "daily.head(2)\n",
    "daily.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplfinance as mpf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-05</th>\n",
       "      <td>133.779999</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>133.809998</td>\n",
       "      <td>117.065437</td>\n",
       "      <td>165936000</td>\n",
       "      <td>133.254297</td>\n",
       "      <td>124.912703</td>\n",
       "      <td>1.066618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2011-07-05  133.779999  134.080002  133.389999  133.809998  117.065437   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2011-07-05  165936000  133.254297  124.912703  1.066618  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = daily.iloc[1:2,:]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "439: avg_dist_between_points = 0.0\n",
      "314: avg_dist_between_points = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(x,type='candle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
